Video Based Human Activity Recognition Surveillance System

Abstract

It is argued that current state-of-the-art methods of home surveillance such as motion detection technology like CCTV intrusion alert are insufficient, in particular to cater the modern need of whole automation with vulnerabilities such as requiring human involvement. We propose an alternative system, a video-based Human Activity Recognition (HAR) approach using the combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) algorithm, to dealwith the identified shortcomings. Our proposal does not require any changes to the existing home security protocols and is easily implemented using only low-cost, commercial-off-the-shelf hardware. We can easily use the traditional surveillance camera for computer vision tasks. We evaluate our approach using real-world activity data collected via video-based sensor. We quantify its effectiveness, by plotting Loss and accuracy curves. Our results show that the video-based HAR approach can provide full automation in home surveillancesystem compared to conventional CCTV motion detectors by an accuracy of more than 93%. Further system’s accuracy can be increased and we can achieve significantly better results by implementing LRCN (Long Term Recurrent Convolution Network) approach.